Weld Seam Identification Algorithm Based on Improved KCF and Peak Tracking
Aiming at the difficulty of seam tracking caused by interference such as smoke and arc light in welding,a seam tracking method based on improved kernelized correlation filters(KCF)is proposed.First-ly,the gray feature is introduced to make the improved KCF algorithm more robust to welding smoke inter-ference.Then,a peak tracking algorithm based on quadratic exponential smoothing method is proposed to predict and track the real-time weld position.Finally,the location of weld feature points is compensated by Hamming window and cosine similarity.The experimental results show that this method has high tracking accuracy under the interference of welding smoke and arc light,and is also suitable for various welding tasks with strong adaptability.
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维普
